Discussing TensorFlow History, Challenges, and Learning Perspective

TensorFlow is an open-source machine learning library originally developed by Google. The solution’s flexible architecture allows for deploying computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

TensorFlow overview: The learning perspective

In this video, Eric Danziger, Senior Engineer at a computer vision startup in San Jose, shared his experience of learning TensorFlow. He started with the MNIST demo and worked up to replicating parts of the “Playing Atari with Deep Reinforcement Learning” paper by Volodymyr Mnih et al.

An end-to-end example of using TensorFlow

In his talk, Delip Rao of Joostware focused on under-the-hood mechanisms of TensorFlow with an actual code example. His goal was to demonstrate various TensorFlow concepts in the context of a working application.

Fireside chat with the Google Brain team

This session with Yaroslav Bulatov and Lukasz Kaiser of the Google Brain team overviews the formation of TensorFlow in brief, provides some examples of the tool applied within Google products, plans for the future, etc. Highlights:

The history behind the project

Examples of TensorFlow behind the Google products

Problems that can be solved with TensorFlow

The most exciting thing about TensorFlow

The feedback from the external stakeholders and actions taken

How TensorFlow becoming open-source changed the roadmap

The plans for the future in terms of multi-GPU cloud deployments

How the “kubernetization” of TensorFlow happens

The DevOps aspect of TensorFlow: issues to handle

The problems TensorFlow is really good at solving or will be able in the future

The biggest challenges TensorFlow has right now and the resolutions to come in the next 3–6 months

Fireside chat: OpenAI and the future of deep learning

Gregory Renard, Chief Visionary Officer at XBrain—the company designing assistance for automakers, talked about OpenAI and the future of deep learning. (OpenAI is a non-profit artificial intelligence research organization founded by recognized machine learning/AI research engineers and scientists.) He highlighted the following aspects:

The problems the automotive / insurance industries face and how XBrain helps to solve them.

How and why such an organization as OpenAI can change the situation, where most of the practitioners in the deep learning are employed by a handful of companies.

The big trends in deep learning and how they are changing the future.

The industries that can benefit from the work of such an organization as OpenAI.

Sophie Turol is passionate about delivering well-structured articles that cater for picky technical audience. With 3+ years in technical writing and 5+ years in editorship, she enjoys collaboration with developers to create insightful, yet intelligible technical tutorials, overviews, and case studies. Sophie is enthusiastic about deep learning solutions—TensorFlow in particular—and PaaS systems, such as Cloud Foundry.

Altoros is a 300+ people strong consultancy that helps Global 2000 organizations with a methodology, training, technology building blocks, and end-to-end solution development. The company turns cloud-native app development, customer analytics, blockchain, and AI into products with a sustainable competitive advantage. Altoros assists enterprises on their way to digital transformation, standing behind some of the world's largest Cloud Foundry deployments.